import importlib.util
import os
import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import (
    is_accelerate_available,
    is_flax_available,
    is_safetensors_available,
    is_tf_available,
    is_torch_available,
)
from . import BaseTransformersCLICommand





def 			lowercase(	UpperCamelCase_       )		->	Optional[int]:







 '''simple docstring'''
 return EnvironmentCommand()





def 			lowercase(	UpperCamelCase_       )		->	Optional[Any]:







 '''simple docstring'''
 return EnvironmentCommand(args.accelerate_config_file       )


class SCREAMING_SNAKE_CASE_		(      __lowerCAmelCase		):







   @staticmethod
   def 						lowerCamelCase_	(     lowerCamelCase_						:						ArgumentParser				):





    """simple docstring"""


    UpperCamelCase			  =      parser.add_parser("""env"""				)
    download_parser.set_defaults(func=lowerCamelCase_				)
    download_parser.add_argument(
        """--accelerate-config_file"""   ,   default=lowerCamelCase_   ,   help="""The accelerate config file to use for the default values in the launching script."""   ,   )
    download_parser.set_defaults(func=lowerCamelCase_				)







   def __init__(     self						:						Optional[int]   ,   lowerCamelCase_						:						Tuple   ,   *lowerCamelCase_						:						Union[str, Any]				):





    """simple docstring"""


    UpperCamelCase			  =      accelerate_config_file







   def 						lowerCamelCase_	(     self						:						Union[str, Any]				):





    """simple docstring"""


    UpperCamelCase			  =      """not installed"""
    if is_safetensors_available():
     import safetensors

     UpperCamelCase			  =      safetensors.__version__
    elif importlib.util.find_spec("""safetensors"""				) is not None:
     import safetensors

     UpperCamelCase			  =      f"""{safetensors.__version__} but is ignored because of PyTorch version too old."""

    UpperCamelCase			  =      """not installed"""
    UpperCamelCase			  =      UpperCamelCase			  =      """not found"""
    if is_accelerate_available():
     import accelerate
     from accelerate.commands.config import default_config_file, load_config_from_file

     UpperCamelCase			  =      accelerate.__version__
     # Get the default from the config file.
     if self._accelerate_config_file is not None or os.path.isfile(lowerCamelCase_				):
      UpperCamelCase			  =      load_config_from_file(self._accelerate_config_file				).to_dict()

     UpperCamelCase			  =      (
         """\n""".join([f"""\t- {prop}: {val}""" for prop, val in accelerate_config.items()]				)
         if isinstance(lowerCamelCase_   ,   lowerCamelCase_				)
         else f"""\t{accelerate_config}"""
     )

    UpperCamelCase			  =      """not installed"""
    UpperCamelCase			  =      """NA"""
    if is_torch_available():
     import torch

     UpperCamelCase			  =      torch.__version__
     UpperCamelCase			  =      torch.cuda.is_available()

    UpperCamelCase			  =      """not installed"""
    UpperCamelCase			  =      """NA"""
    if is_tf_available():
     import tensorflow as tf

     UpperCamelCase			  =      tf.__version__
     try:
      # deprecated in v2.1
      UpperCamelCase			  =      tf.test.is_gpu_available()
     except AttributeError:
      # returns list of devices, convert to bool
      UpperCamelCase			  =      bool(tf.config.list_physical_devices("""GPU"""				)				)

    UpperCamelCase			  =      """not installed"""
    UpperCamelCase			  =      """not installed"""
    UpperCamelCase			  =      """not installed"""
    UpperCamelCase			  =      """NA"""
    if is_flax_available():
     import flax
     import jax
     import jaxlib

     UpperCamelCase			  =      flax.__version__
     UpperCamelCase			  =      jax.__version__
     UpperCamelCase			  =      jaxlib.__version__
     UpperCamelCase			  =      jax.lib.xla_bridge.get_backend().platform

    UpperCamelCase			  =      {
        """`transformers` version""": version,
        """Platform""": platform.platform(),
        """Python version""": platform.python_version(),
        """Huggingface_hub version""": huggingface_hub.__version__,
        """Safetensors version""": f"""{safetensors_version}""",
        """Accelerate version""": f"""{accelerate_version}""",
        """Accelerate config""": f"""{accelerate_config_str}""",
        """PyTorch version (GPU?)""": f"""{pt_version} ({pt_cuda_available})""",
        """Tensorflow version (GPU?)""": f"""{tf_version} ({tf_cuda_available})""",
        """Flax version (CPU?/GPU?/TPU?)""": f"""{flax_version} ({jax_backend})""",
        """Jax version""": f"""{jax_version}""",
        """JaxLib version""": f"""{jaxlib_version}""",
        """Using GPU in script?""": """<fill in>""",
        """Using distributed or parallel set-up in script?""": """<fill in>""",
    }

    print("""\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n"""				)
    print(self.format_dict(lowerCamelCase_				)				)

    return info







   @staticmethod
   def 						lowerCamelCase_	(     lowerCamelCase_						:						Optional[int]				):





    """simple docstring"""


    return "\n".join([f"""- {prop}: {val}""" for prop, val in d.items()]				) + "\n"

import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class SCREAMING_SNAKE_CASE_		(      __lowerCAmelCase		):
   __lowerCAmelCase           =				["""image_processor""", """tokenizer"""]
   __lowerCAmelCase           =				"""LayoutLMv3ImageProcessor"""
   __lowerCAmelCase           =				("""LayoutLMv3Tokenizer""", """LayoutLMv3TokenizerFast""")







   def __init__(     self						:						Dict   ,   lowerCamelCase_						:						Union[str, Any]=None   ,   lowerCamelCase_						:						Dict=None   ,   **lowerCamelCase_						:						str				):





    """simple docstring"""


    UpperCamelCase			  =      None
    if "feature_extractor" in kwargs:
     warnings.warn(
         """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
         """ instead."""   ,   lowerCamelCase_   ,   )
     UpperCamelCase			  =      kwargs.pop("""feature_extractor"""				)

    UpperCamelCase			  =      image_processor if image_processor is not None else feature_extractor
    if image_processor is None:
     raise ValueError("""You need to specify an `image_processor`."""				)
    if tokenizer is None:
     raise ValueError("""You need to specify a `tokenizer`."""				)

    super().__init__(lowerCamelCase_   ,   lowerCamelCase_				)







   def __call__(     self						:						int   ,   lowerCamelCase_						:						str   ,   lowerCamelCase_						:						Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None   ,   lowerCamelCase_						:						Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None   ,   lowerCamelCase_						:						Union[List[List[int]], List[List[List[int]]]] = None   ,   lowerCamelCase_						:						Optional[Union[List[int], List[List[int]]]] = None   ,   lowerCamelCase_						:						bool = True   ,   lowerCamelCase_						:						Union[bool, str, PaddingStrategy] = False   ,   lowerCamelCase_						:						Union[bool, str, TruncationStrategy] = None   ,   lowerCamelCase_						:						Optional[int] = None   ,   lowerCamelCase_						:						int = 0   ,   lowerCamelCase_						:						Optional[int] = None   ,   lowerCamelCase_						:						Optional[bool] = None   ,   lowerCamelCase_						:						Optional[bool] = None   ,   lowerCamelCase_						:						bool = False   ,   lowerCamelCase_						:						bool = False   ,   lowerCamelCase_						:						bool = False   ,   lowerCamelCase_						:						bool = False   ,   lowerCamelCase_						:						bool = True   ,   lowerCamelCase_						:						Optional[Union[str, TensorType]] = None   ,   **lowerCamelCase_						:						Optional[int]   ,   ):





    """simple docstring"""


    if self.image_processor.apply_ocr and (boxes is not None):
     raise ValueError(
         """You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True."""				)

    if self.image_processor.apply_ocr and (word_labels is not None):
     raise ValueError(
         """You cannot provide word labels if you initialized the image processor with apply_ocr set to True."""				)

    # first, apply the image processor
    UpperCamelCase			  =      self.image_processor(images=lowerCamelCase_   ,   return_tensors=lowerCamelCase_				)

    # second, apply the tokenizer
    if text is not None and self.image_processor.apply_ocr and text_pair is None:
     if isinstance(lowerCamelCase_   ,   lowerCamelCase_				):
      UpperCamelCase			  =      [text]  # add batch dimension (as the image processor always adds a batch dimension)
     UpperCamelCase			  =      features["""words"""]

    UpperCamelCase			  =      self.tokenizer(
        text=text if text is not None else features["""words"""]   ,   text_pair=text_pair if text_pair is not None else None   ,   boxes=boxes if boxes is not None else features["""boxes"""]   ,   word_labels=lowerCamelCase_   ,   add_special_tokens=lowerCamelCase_   ,   padding=lowerCamelCase_   ,   truncation=lowerCamelCase_   ,   max_length=lowerCamelCase_   ,   stride=lowerCamelCase_   ,   pad_to_multiple_of=lowerCamelCase_   ,   return_token_type_ids=lowerCamelCase_   ,   return_attention_mask=lowerCamelCase_   ,   return_overflowing_tokens=lowerCamelCase_   ,   return_special_tokens_mask=lowerCamelCase_   ,   return_offsets_mapping=lowerCamelCase_   ,   return_length=lowerCamelCase_   ,   verbose=lowerCamelCase_   ,   return_tensors=lowerCamelCase_   ,   **lowerCamelCase_   ,   )

    # add pixel values
    UpperCamelCase			  =      features.pop("""pixel_values"""				)
    if return_overflowing_tokens is True:
     UpperCamelCase			  =      self.get_overflowing_images(lowerCamelCase_   ,   encoded_inputs["""overflow_to_sample_mapping"""]				)
    UpperCamelCase			  =      images

    return encoded_inputs







   def 						lowerCamelCase_	(     self						:						str   ,   lowerCamelCase_						:						Union[str, Any]   ,   lowerCamelCase_						:						Dict				):





    """simple docstring"""


    UpperCamelCase			  =      []
    for sample_idx in overflow_to_sample_mapping:
     images_with_overflow.append(images[sample_idx]				)

    if len(lowerCamelCase_				) != len(lowerCamelCase_				):
     raise ValueError(
         """Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"""
         f""" {len(lowerCamelCase_				)} and {len(lowerCamelCase_				)}"""				)

    return images_with_overflow







   def 						lowerCamelCase_	(     self						:						Union[str, Any]   ,   *lowerCamelCase_						:						Any   ,   **lowerCamelCase_						:						Union[str, Any]				):





    """simple docstring"""


    return self.tokenizer.batch_decode(*lowerCamelCase_   ,   **lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						List[Any]   ,   *lowerCamelCase_						:						Dict   ,   **lowerCamelCase_						:						str				):





    """simple docstring"""


    return self.tokenizer.decode(*lowerCamelCase_   ,   **lowerCamelCase_				)







   @property
   def 						lowerCamelCase_	(     self						:						int				):





    """simple docstring"""


    return ["input_ids", "bbox", "attention_mask", "pixel_values"]







   @property
   def 						lowerCamelCase_	(     self						:						Optional[Any]				):





    """simple docstring"""


    warnings.warn(
        """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""   ,   lowerCamelCase_   ,   )
    return self.image_processor_class







   @property
   def 						lowerCamelCase_	(     self						:						Optional[Any]				):





    """simple docstring"""


    warnings.warn(
        """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""   ,   lowerCamelCase_   ,   )
    return self.image_processor
